In this paper, we proposed a semi-supervised fair representation learning method named Semi-FairVAE, which can effectively reduce the dependency of adversarial fair representation learning methods on labeled samples with sensitive attributes via a semi-supervised adversarial variational autoencoder.
Different from the standard semi-supervised variational autoencoder (Semi-VAE) that incorporates the predicted label as the latent variable for input construction, in fair adversarial learning methods the predicted attributes are enforced to be random and it is not suitable to directly take them as latent variables.
Motivated by the decomposed adversarial learning method proposed in [1]}, we use a bias-aware model to capture the bias information related to sensitive attributes in the input data and use a bias-free model to learn fair representations with bias information eliminated.
The bias-aware model is used to accurately infer the sensitive attributes by training in an attribute prediction task, while the bias-free model is used to learn bias-independent feature representations by applying an adversarial learning module to its output, where the hidden representations learned by the two models are regularized to be orthogonal to further remove bias information from the bias-free model.
In addition, the soft attribute labels predicted by the two models and the feature learned by a VAE encoder are further incorporated as the latent variables in a semi-supervised variational autoencoder to reconstruct the input data.
Note that on samples with labeled attributes, the attribute labels predicted by the bias-aware and bias-free models are respectively replaced by the real labels and soft labels with uniform class probabilities, which aim to provide supervision information on the real and adversarial label distributions to help the input reconstruction in generative semi-supervised learning.
To further encourage the bias-free model to be attribute insensitive, on samples without attribute labels the attribute predictions from the bias-free model are regularized to have higher entropy so that less sensitive attribution information can be encoded in the bias-free feature representations.
Extensive experiments on two datasets for different tasks validate that our proposed Semi-FairVAE approach can achieve both good accuracy and fairness under limited data with observed sensitive attribute labels.
